%load_ext autoreload

The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

%autoreload
import anndata as ad
import scanpy as sc
import matplotlib.pyplot as plt
import squidpy as sq
import numpy as np

import sys
sys.path.append('../../')
import scrofit.plotting as pl

sample = 'V11T17-102'
in_dir = f'../../../data/MSA_data/{sample}'

st_adata = ad.read_h5ad(f'{in_dir}/{sample}_st_pp.adata')
sm_adata = ad.read_h5ad(f'{in_dir}/{sample}_sm_pp.adata')
mdata = ad.read_h5ad(f'{in_dir}/{sample}_m_pp.adata')


sm_adata.obsm['SMA\nSM OCS'] = sm_adata.obsm['spatial']
st_adata.obsm['SMA\nST OCS'] = st_adata.obsm['spatial']
sc.pl.spatial(st_adata, basis='SMA\nST OCS')



mymap = {'unk': np.nan, 'unk2': np.nan}
st_adata.obs['Region'] = st_adata.obs['RegionLoupe'].apply(lambda x: mymap.get(x, x))
pl.ocs_spatial_scatter(st_adata, 'ST', color=['Region'], img=True, shape=None, library_id=sample,
                      size=0.5)
mdata.obs['ST_Region'] = mdata.obs['ST_RegionLoupe'].apply(lambda x: mymap.get(x, x))
pl.ccs_spatial_scatter(mdata, 'ST', color=['ST_Region'], img=True, shape=None, library_id=sample,
                      size=0.5)
pl.embed(mdata, color='ST_Region', figsize=(2.8, 3))
pl.ocs_spatial_scatter(st_adata, 'ST', color=['dopamine'], img=True, shape=None, library_id=sample,
                      size=0.5, palette='Set2')

pl.ccs_spatial_scatter(mdata, 'ST', color=['ST_dopamine'], img=True, shape=None, library_id=sample,
                       size=0.5, palette='Set2')

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\anndata\_core\anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
C:\Users\User\AppData\Local\Temp\ipykernel_114756\1202873127.py:22: FutureWarning: Use `squidpy.pl.spatial_scatter` instead.

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

sm_adata

AnnData object with n_obs × n_vars = 15813 × 1538
    obs: 'sample', 'block', 'n_genes', 'ST_RegionLoupe', 'ST_MSN.D2.PenkNeg_5', 'ST_MSN.D1.TacNeg_3', 'ST_MSN.D2_C_18', 'ST_MSN.D1.TacPosB_6', 'ST_MSN.D1.TacPosA_9', 'ST_MSN.D2.PenkPos_2', 'ST_ST_id', 'LY6H', 'THY1', 'VSNL1', 'PCDH11X', 'MBP', 'PLP1', 'CHN1', 'SYNPR', 'NCDN', 'MAP2', 'GDA', 'HPCAL4', 'RTN1', 'NNAT', 'TSPAN7', 'PEG10', 'SCG2', 'TF', 'CRYM', 'GFAP', 'MT1G'
    uns: 'spatial'
    obsm: 'XY_spatial_euclidean_dist', 'mappingflow_MCMF', 'spatial', 'spatial_MCMF_map', 'spatial_normalized', 'spatial_rir', 'spatial_rir_filtered', 'SMA\nSM OCS'
    layers: 'log1p', 'raw'

%autoreload
for color in st_adata.obs.columns:
    if not color.startswith('MSN'):
        continue
    pl.ocs_spatial_scatter(st_adata, 'ST', color=color, img=True, shape=None, 
                          library_id=sample, cmap='OrRd', size=0.5)

%autoreload
for gene in ['SCG2', 'CHN1', 'NNAT', 'GFAP', 'MBP', 'TF', 'PLP1']:
    pl.ocs_spatial_scatter(st_adata, 'ST', color=gene, img=True, shape=None, 
                          library_id=sample, cmap='Spectral_r', size=0.5)

%autoreload
pl.ocs_spatial_scatter(sm_adata, 'SM', color='mz_674.2805', img=False, shape=None, library_id=sample,
                      cmap='viridis', size=0.5)
pl.ccs_spatial_scatter(mdata, 'SM', color='mz_674.2805', img=False, shape=None, library_id=sample,
                      cmap='plasma', size=0.5)

pl.embed(mdata, color='mz_674.2805', cmap='plasma', figsize=(3.2, 3), colorbar_loc='right')

%autoreload

pl.ocs_spatial_scatter(st_adata, 'ST', color=['Cell_Type'], img=True, shape=None, library_id=sample,
                       palette='Accent', size=0.5)
pl.ccs_spatial_scatter(mdata, 'ST', color=['ST_Cell_Type'], img=True, shape=None, library_id=sample,
                       palette='Accent', size=0.5)
pl.embed(mdata, color='ST_Cell_Type', palette='Accent', figsize=(2.8, 3))

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

st_adata.obsm

AxisArrays with keys: spatial, spatial_normalized, spatial_rir, spatial_rir_filtered, SMA
ST OCS

mdata.obsm

AxisArrays with keys: SM_XY_spatial_euclidean_dist, SM_mappingflow_MCMF, SM_spatial, SM_spatial_MCMF_map, SM_spatial_normalized, SM_spatial_rir, SM_spatial_rir_filtered, ST_spatial, ST_spatial_normalized, ST_spatial_rir, ST_spatial_rir_filtered, X_pca, X_umap

%autoreload

x_min=35000
x_max=x_min+5000
y_min=35000
y_max=y_min+5000

pl.ccs_spatial_zoom(
    st_adata, mdata,
    metabolite="mz_674.2805",
    cell_type_col="Cell_Type",
    st_coord_key="spatial_rir",
    sm_coord_key="SM_spatial_rir",
    cell_dot_size=250,
    x_min=x_min, x_max=x_max, 
    y_min=y_min, y_max=y_max,
    figsize=(4,4),
    st_cmap="Accent",
    m_cmap='plasma'    
)

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:233: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

%autoreload

pl.ccs_spatial_zoom(
    st_adata, mdata,
    metabolite="mz_674.2805",
    cell_type_col="Region",
    st_coord_key="spatial_rir",
    sm_coord_key="SM_spatial_rir",
    cell_dot_size=250,
    gene_dot_size=10,
    x_min=x_min, x_max=x_max, 
    y_min=y_min, y_max=y_max,
    figsize=(4,4),
    st_cmap=None,
    m_cmap='plasma'
)

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:233: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

%autoreload
import seaborn as sns
import pandas as pd

dopamine = 'Dopamine (mz_674.2805)'
mymap = {'unk': np.nan, 'unk2': np.nan}
sm_adata.obs['Region'] = sm_adata.obs['ST_RegionLoupe'].apply(lambda x: mymap.get(x, x))
mymap = st_adata.obs['Cell_Type'].to_dict()
sm_adata.obs['Cell_Type'] = sm_adata.obs['ST_ST_id'].apply(lambda x: mymap[x])
sm_adata.obs['New_Cell_Type']  = sm_adata.obs['Cell_Type'].apply(lambda x: np.nan if x in ['Inhib', 'Inhib_MSN'] else x)
custom_order = ['MSN', 'Mi.MiR.Ma', 'Astro', 'OPC', 'Oligo']
sm_adata.obs['New_Cell_Type']  = sm_adata.obs['New_Cell_Type'].astype(pd.CategoricalDtype(categories=custom_order, ordered=True))


sm_adata.obs[dopamine] = dict(zip(sm_adata.obs.index, sm_adata[:, 'mz_674.2805'].X.reshape(-1)))

compare_type = 'New_Cell_Type'
comparisons = [
    ("MSN", "Astro"),    
    ("MSN", "Mi.MiR.Ma"),    
    ("MSN", "OPC"),    
    ("MSN", "Oligo"),     
]
base_colors = sns.color_palette('Accent', 8).as_hex()
palette = dict(zip(np.sort(sm_adata.obs['Cell_Type'].dropna().unique()), base_colors[:3] + base_colors[-4:]))
pl.violin(sm_adata, compare_type, dopamine, comparisons,
             fig_size=(3, 3), 
             palette=palette,
             text_x_angle=20)

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:296: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

%autoreload
compare_type = 'Region'
comparisons = [
    #("ACB", "CI"),    
    #("ACB", "Cd"),    
    ("Cd", "CI"),    
]
pl.violin(sm_adata[sm_adata.obs['Region'] != 'ACB'], compare_type, dopamine, comparisons,
             fig_size=(3, 3),
             text_x_angle=90,
             palette=sc.pl.palettes.default_20[1:])

import pandas as pd
df = pd.read_csv('../../../data/MSA_data/corHstr_onlyCdSpots.csv', index_col=0)
SMA_cor_df = df[(df['FDR'] < 0.05) & (df['rho'].abs() > 0.2)]

SMA_cor_df['Region'] = 'Cd'
SMA_cor_df['Method'] = 'SMA'
SMA_cor_df

C:\Users\User\AppData\Local\Temp\ipykernel_114756\3254590528.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
C:\Users\User\AppData\Local\Temp\ipykernel_114756\3254590528.py:6: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

	dopamine	gene	rho	Pvalue	FDR	sig	corlabel	InvLog10FDR	Region	Method
348	Dopamine	NCDN	0.244814	8.910519e-101	1.517065e-97	PosCor	NCDN	96.818996	Cd	SMA
395	Dopamine	HPCAL4	0.210742	1.407301e-74	1.198004e-71	PosCor	HPCAL4	70.921542	Cd	SMA
1550	Dopamine	VSNL1	0.236569	4.872488e-94	6.787375e-91	PosCor	VSNL1	90.168298	Cd	SMA
2148	Dopamine	CHN1	0.263871	2.375161e-117	1.819730e-113	PosCor	CHN1	112.739993	Cd	SMA
2290	Dopamine	MAP2	0.200022	3.214671e-67	2.239018e-64	PosCor	MAP2	63.649942	Cd	SMA
2368	Dopamine	SCG2	0.326443	4.317302e-182	6.615401e-178	PosCor	SCG2	177.179444	Cd	SMA
2845	Dopamine	SYNPR	0.255258	1.098729e-109	3.367164e-106	PosCor	SYNPR	105.472736	Cd	SMA
3064	Dopamine	TF	-0.234649	1.659563e-92	2.119124e-89	NegCor	TF	88.673844	Cd	SMA
5685	Dopamine	PEG10	0.229432	2.058726e-88	2.253275e-85	PosCor	PEG10	84.647186	Cd	SMA
6483	Dopamine	LY6H	0.229322	2.504924e-88	2.558863e-85	PosCor	LY6H	84.591953	Cd	SMA
6715	Dopamine	GDA	0.204074	5.952055e-70	4.343016e-67	PosCor	GDA	66.362209	Cd	SMA
8509	Dopamine	THY1	0.250591	1.185839e-105	3.028434e-102	PosCor	THY1	101.518782	Cd	SMA
9885	Dopamine	RTN1	0.205265	9.126335e-71	6.992142e-68	PosCor	RTN1	67.155390	Cd	SMA
10849	Dopamine	CRYM	0.247718	3.268268e-103	7.154239e-100	PosCor	CRYM	99.145437	Cd	SMA
11027	Dopamine	MT1G	0.212344	1.029377e-75	9.278317e-73	PosCor	MT1G	72.032531	Cd	SMA
11788	Dopamine	GFAP	-0.262491	4.203159e-116	2.146833e-112	NegCor	GFAP	111.668202	Cd	SMA
12343	Dopamine	MBP	-0.246967	1.403670e-102	2.688555e-99	NegCor	MBP	98.570481	Cd	SMA
13619	Dopamine	NNAT	0.258008	4.219107e-112	1.616235e-108	PosCor	NNAT	107.791496	Cd	SMA
14392	Dopamine	TSPAN7	0.239372	2.667464e-96	4.087356e-93	PosCor	TSPAN7	92.388558	Cd	SMA
14576	Dopamine	PCDH11X	0.206412	1.480592e-71	1.194059e-68	PosCor	PCDH11X	67.922974	Cd	SMA
14624	Dopamine	PLP1	-0.233789	7.979968e-92	9.405927e-89	NegCor	PLP1	88.026598	Cd	SMA
14768	Dopamine	PNMA5	0.223717	4.790115e-84	4.587434e-81	PosCor	PNMA5	80.338430	Cd	SMA

from scipy.stats import pearsonr

data_list = []
for region in sm_adata.obs['Region'].unique():    
    df = sm_adata[sm_adata.obs['Region'] == region].obs
    if df.shape[0] == 0:
        continue
    for gene in SMA_cor_df['gene'].tolist():
        if gene not in df.columns:
            continue
        corr, p_value = pearsonr(df[dopamine], df[gene])
        data_list.append((region, 'Dopamine', gene, corr, p_value))
df = pd.DataFrame(data_list, columns=['Region', 'dopamine', 'gene', 'rho', 'Pvalue'])
df['Method'] = 'SCRoFit'
bench_cor_df = pd.concat([SMA_cor_df[df.columns], df])
bench_cor_df['Type'] = bench_cor_df['Region'] + '(' + bench_cor_df['Method'] + ')'
bench_cor_df['InvLog10Pvalue'] = -np.log10(bench_cor_df['Pvalue'])

bench_cor_df = bench_cor_df[bench_cor_df['Pvalue'] < 0.05]


from matplotlib.colors import Normalize

# Normalize the color values to set the midpoint to zero
z = bench_cor_df['rho']
norm = Normalize(vmin=-np.max(np.abs(z)), vmax=np.max(np.abs(z)))


fig = plt.figure(figsize=(7, 1.8)) 
sns.scatterplot(
    data=bench_cor_df.sort_values(by='rho'), 
    y="Type", x="gene", 
    #order=['Cd(SCRoFit)', 'Cd(SMA)', 'CI(SCRoFit)', 'ACB', 'SCRoFit'],
    hue='rho', hue_norm=norm,
    size='InvLog10Pvalue',
    palette='coolwarm'
)
plt.xlabel(None)
plt.xticks(rotation=45, ha='right')
plt.ylabel(None)
plt.legend().set_visible(False)  # Hide the legend
plt.tight_layout()
plt.show()

from scipy.stats import pearsonr

data_list = []
for cell_type in sm_adata.obs['Cell_Type'].unique():    
    df = sm_adata[sm_adata.obs['Cell_Type'] == cell_type].obs
    if df.shape[0] == 0:
        continue
    for gene in SMA_cor_df['gene'].tolist():
        if gene not in df.columns:
            continue
        corr, p_value = pearsonr(df[dopamine], df[gene])
        data_list.append((cell_type, 'Dopamine', gene, corr, p_value))
cor_df = pd.DataFrame(data_list, columns=['Cell Type', 'dopamine', 'gene', 'rho', 'Pvalue'])
cor_df['Method'] = 'SCRoFit'
cor_df = cor_df[cor_df['Pvalue'] < 0.05]
cor_df['InvLog10Pvalue'] = -np.log10(cor_df['Pvalue'])


fig = plt.figure(figsize=(7,3)) 
z = cor_df['rho']
norm = Normalize(vmin=-np.max(np.abs(z)), vmax=np.max(np.abs(z)))

sns.scatterplot(
    data=cor_df.sort_values(by='rho'), 
    y="Cell Type", x="gene", 
    #order=['Cd(SCRoFit)', 'Cd(SMA)', 'CI(SCRoFit)', 'ACB', 'SCRoFit'],
    hue='rho', hue_norm=norm,
    size='InvLog10Pvalue',
    palette='coolwarm'
)
plt.xlabel(None)
plt.ylabel(None)
plt.xticks(rotation=45, ha='right')
plt.legend().set_visible(False)  # Hide the legend
plt.tight_layout()
plt.show()

C:\Users\User\AppData\Local\Temp\ipykernel_114756\3588643302.py:11: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.

bench_cor_df['Annotation'] = bench_cor_df['Region']
bench_cor_df['Function Annotation'] = 'Region'
cor_df['Annotation'] = cor_df['Cell Type']
cor_df['Function Annotation'] = 'Cell Type'

cols = ['Function Annotation', 'Annotation', 'dopamine', 'gene', 'rho', 'Pvalue', 'Method',
       'InvLog10Pvalue']
df = pd.concat([bench_cor_df[cols], cor_df[cols]])
df = df.sort_values(by=['Function Annotation', 'Annotation', 'rho'], ascending=[False, True, True])
z = df['rho']
norm = Normalize(vmin=-np.max(np.abs(z)), vmax=np.max(np.abs(z)))

fig = plt.figure(figsize=(4,5)) 
sns.scatterplot(
    data=df[df['Method'] == 'SCRoFit'], 
    x="Annotation", y="gene", 
    #order=['Cd(SCRoFit)', 'Cd(SMA)', 'CI(SCRoFit)', 'ACB', 'SCRoFit'],
    hue='rho', hue_norm=norm,
    size='InvLog10Pvalue',
    palette='coolwarm'
)
plt.xlabel(None)
plt.ylabel(None)
plt.xticks(rotation=60, ha='right')
#plt.legend().set_visible(False)  # Hide the legend
plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
#plt.tight_layout()
plt.show()

import pandas as pd
import numpy as np
from plotnine import *

df = df.sort_values(
    by=['Function Annotation', 'Annotation', 'rho'],
    ascending=[False, True, True]
).copy()

df['gene'] = pd.Categorical(df['gene'], categories=df['gene'].unique(), ordered=True)
df = df[df['Annotation'].isin(['CI', 'Cd', 'MSN', 'Astro', 'OPC', 'Oligo'])]
df['Annotation'] = pd.Categorical(df['Annotation'],categories=['CI', 'Cd', 'MSN', 'Astro', 'OPC', 'Oligo'],ordered=True)

max_abs = df['rho'].abs().max()
p = (
    ggplot(df[df['Method'] == 'SCRoFit'],
           aes(y='Annotation', x='gene',
               color='rho',
               size='InvLog10Pvalue'))
    + facet_grid('Function Annotation ~ .', 
                 scales='free', space='free')
    + geom_point()
    + scale_color_gradient2(
        low="blue", mid="white", high="red",
        midpoint=0,
        limits=(-max_abs, max_abs)
      )
    + scale_size_area(max_size=5)
    + theme_bw()
    + theme(
        figure_size=(5, 4),  
        axis_text_y=element_text(angle=45, hjust=1),
        axis_text_x=element_text(angle=90, hjust=-1),
        strip_background=element_blank(),
      )
    + labs(
        color='rho',
        size='-log10(p)',
        x='',
        y=''
      )   
)

p

st_adata[st_adata.obs['Region'] == 'ACB'].obsm['spatial_rir'].copy().min(axis=0), \
st_adata[st_adata.obs['Region'] == 'ACB'].obsm['spatial_rir'].copy().max(axis=0)

(array([  5396, 107920], dtype=int64), array([ 42228, 157478], dtype=int64))

%autoreload

x_min = 17000
x_max = x_min + 5000
y_min = 120000
y_max = y_min + 5000
# Usage example
pl.ccs_spatial_zoom(
    st_adata, mdata,
    metabolite="mz_674.2805", 
    gene="GFAP",
    cell_type_col="Region",
    st_coord_key="spatial_rir",
    sm_coord_key="SM_spatial_rir",
    cell_dot_size=250,
    gene_dot_size=10,
    x_min=x_min, x_max=x_max, 
    y_min=y_min, y_max=y_max,
    figsize=(4, 4),
    st_cmap=None,
    g_cmap='Spectral_r',
    m_cmap='plasma'
)

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:233: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

%autoreload

pl.plot_gene_cell_type(st_adata, mdata, 'GFAP', 'Region', 'ACB')

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:367: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:370: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\anndata\_core\anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:375: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:376: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:377: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden

print(st_adata[st_adata.obs['Region'] == 'CI'].obsm['spatial_rir'].copy().min(axis=0), \
st_adata[st_adata.obs['Region'] == 'CI'].obsm['spatial_rir'].copy().max(axis=0))

x_min = 30120
x_max = x_min + 5000
y_min = 80000
y_max = y_min + 5000

pl.ccs_spatial_zoom(
    st_adata, mdata,
    metabolite="mz_674.2805", 
    gene="TF",
    cell_type_col="Region",
    st_coord_key="spatial_rir",
    sm_coord_key="SM_spatial_rir",
    cell_dot_size=250,
    gene_dot_size=10,
    x_min=x_min, x_max=x_max, 
    y_min=y_min, y_max=y_max,
    figsize=(4, 4),
    st_cmap=None,
    g_cmap='Spectral_r',
    m_cmap='plasma'
)

[25120  3341] [ 42534 131249]

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:233: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

%autoreload
pl.plot_gene_cell_type(st_adata, mdata, 'TF', 'Region', 'CI')

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:367: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:370: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\anndata\_core\anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:375: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:376: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:377: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden

%autoreload
pl.plot_gene_cell_type(st_adata, mdata, 'NCDN', 'Region', 'Cd')

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:367: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:370: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\anndata\_core\anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:375: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:376: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:377: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden

%autoreload
pl.plot_gene_cell_type(st_adata, mdata, 'SCG2', 'Region', 'Cd')

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:367: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:370: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\anndata\_core\anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:375: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:376: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:377: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden

%autoreload
print(st_adata[st_adata.obs['Cell_Type'] == 'Astro'].obsm['spatial_rir'].copy().min(axis=0), \
st_adata[st_adata.obs['Cell_Type'] == 'Astro'].obsm['spatial_rir'].copy().max(axis=0))

x_min = 15000
x_max = x_min + 5000
y_min = 150000
y_max = y_min + 5000
# Usage example
pl.ccs_spatial_zoom(
    st_adata, mdata,
    metabolite="mz_674.2805", 
    gene="MBP",
    cell_type_col="Cell_Type",
    st_coord_key="spatial_rir",
    sm_coord_key="SM_spatial_rir",
    cell_dot_size=250,
    gene_dot_size=10,
    x_min=x_min, x_max=x_max, 
    y_min=y_min, y_max=y_max,
    figsize=(4, 4),
    st_cmap='Accent',
    g_cmap='Spectral_r',
    m_cmap='plasma'
)

[5256 3777] [ 42121 176669]

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:233: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

%autoreload
pl.plot_gene_cell_type(st_adata, mdata, 'MBP', 'Cell_Type', 'Astro')

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:367: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:370: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\anndata\_core\anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:375: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:376: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:377: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden

%autoreload
pl.plot_gene_cell_type(st_adata, mdata, 'SCG2', 'Cell_Type', 'MSN')

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:367: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:370: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\anndata\_core\anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:375: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:376: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:377: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden

%autoreload
print(st_adata[st_adata.obs['Cell_Type'] == 'MSN'].obsm['spatial_rir'].copy().min(axis=0), \
st_adata[st_adata.obs['Cell_Type'] == 'MSN'].obsm['spatial_rir'].copy().max(axis=0))

x_min = 25120  
x_max = x_min + 5000
y_min = 100000
y_max = y_min + 5000
# Usage example
pl.ccs_spatial_zoom(
    st_adata, mdata,
    metabolite="mz_674.2805", 
    gene="NCDN",
    cell_type_col="Cell_Type",
    st_coord_key="spatial_rir",
    sm_coord_key="SM_spatial_rir",
    cell_dot_size=250,
    gene_dot_size=10,
    x_min=x_min, x_max=x_max, 
    y_min=y_min, y_max=y_max,
    figsize=(4, 4),
    st_cmap='Accent',
    g_cmap='Spectral_r',
    m_cmap='plasma'
)

[5373 3267] [ 42275 167991]

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:233: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

%autoreload
pl.plot_gene_cell_type(st_adata, mdata, 'NCDN', 'Cell_Type', 'MSN')

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:367: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:370: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\anndata\_core\anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:375: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:376: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:377: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden

%autoreload
pl.plot_gene_cell_type(st_adata, mdata, 'PLP1', 'Cell_Type', 'Oligo')

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:367: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\squidpy\pl\_spatial_utils.py:976: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap', 'norm' will be ignored

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:370: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.

C:\Users\User\AppData\Roaming\Python\Python312\site-packages\anndata\_core\anndata.py:1756: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:375: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:376: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden
C:\Users\User\AppData\Roaming\Python\Python312\site-packages\scipy\sparse\_index.py:151: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
c:\Users\User\Dropbox\workspace\1_SMST\SCRoFit\docs\tutorial\../..\scrofit\plotting.py:377: ImplicitModificationWarning: Modifying `X` on a view results in data being overridden

bench_cor_df[bench_cor_df['gene'].isin(['GFAP', 'TF', 'NCDN', 'SCG2', 'MBP', 'PLP1'])]

	Region	dopamine	gene	rho	Pvalue	Method	Type	InvLog10Pvalue	Annotation	Function Annotation
348	Cd	Dopamine	NCDN	0.244814	8.910519e-101	SMA	Cd(SMA)	100.050097	Cd	Region
2368	Cd	Dopamine	SCG2	0.326443	4.317302e-182	SMA	Cd(SMA)	181.364788	Cd	Region
3064	Cd	Dopamine	TF	-0.234649	1.659563e-92	SMA	Cd(SMA)	91.780006	Cd	Region
11788	Cd	Dopamine	GFAP	-0.262491	4.203159e-116	SMA	Cd(SMA)	115.376424	Cd	Region
12343	Cd	Dopamine	MBP	-0.246967	1.403670e-102	SMA	Cd(SMA)	101.852735	Cd	Region
14624	Cd	Dopamine	PLP1	-0.233789	7.979968e-92	SMA	Cd(SMA)	91.097999	Cd	Region
0	Cd	Dopamine	NCDN	0.232060	1.737992e-94	SCRoFit	Cd(SCRoFit)	93.759952	Cd	Region
5	Cd	Dopamine	SCG2	0.192807	2.956723e-65	SCRoFit	Cd(SCRoFit)	64.529189	Cd	Region
7	Cd	Dopamine	TF	-0.139372	1.220900e-34	SCRoFit	Cd(SCRoFit)	33.913320	Cd	Region
15	Cd	Dopamine	GFAP	-0.125049	3.667300e-28	SCRoFit	Cd(SCRoFit)	27.435654	Cd	Region
16	Cd	Dopamine	MBP	-0.178574	4.341583e-56	SCRoFit	Cd(SCRoFit)	55.362352	Cd	Region
20	Cd	Dopamine	PLP1	-0.151670	8.900975e-41	SCRoFit	Cd(SCRoFit)	40.050562	Cd	Region
21	CI	Dopamine	NCDN	0.040255	2.859989e-02	SCRoFit	CI(SCRoFit)	1.543636	CI	Region
26	CI	Dopamine	SCG2	0.129088	1.839477e-12	SCRoFit	CI(SCRoFit)	11.735306	CI	Region
28	CI	Dopamine	TF	-0.503951	2.912170e-190	SCRoFit	CI(SCRoFit)	189.535783	CI	Region
36	CI	Dopamine	GFAP	-0.453098	1.153272e-149	SCRoFit	CI(SCRoFit)	148.938068	CI	Region
37	CI	Dopamine	MBP	-0.457587	5.602338e-153	SCRoFit	CI(SCRoFit)	152.251631	CI	Region
41	CI	Dopamine	PLP1	-0.475458	1.132699e-166	SCRoFit	CI(SCRoFit)	165.945886	CI	Region
42	ACB	Dopamine	NCDN	0.173216	1.763107e-12	SCRoFit	ACB(SCRoFit)	11.753721	ACB	Region
47	ACB	Dopamine	SCG2	0.133786	5.637002e-08	SCRoFit	ACB(SCRoFit)	7.248952	ACB	Region
49	ACB	Dopamine	TF	-0.211509	5.436318e-18	SCRoFit	ACB(SCRoFit)	17.264695	ACB	Region
57	ACB	Dopamine	GFAP	-0.558419	1.059766e-134	SCRoFit	ACB(SCRoFit)	133.974790	ACB	Region
58	ACB	Dopamine	MBP	-0.292058	1.629507e-33	SCRoFit	ACB(SCRoFit)	32.787944	ACB	Region
62	ACB	Dopamine	PLP1	-0.144015	4.935206e-09	SCRoFit	ACB(SCRoFit)	8.306695	ACB	Region